# Hybrid Ranking Network for Text-to-SQL

arXiv: https://arxiv.org/abs/2008.04759 · HTML: https://mlsanity.com/paper/2008.04759/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2020.

## Built on by (most cited first, 30 of 33)

- [Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL](https://mlsanity.com/paper/2406.08426/) (2024)
- [A Survey on Employing Large Language Models for Text-to-SQL Tasks](https://mlsanity.com/paper/2407.15186/) (2024)
- [The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models](https://mlsanity.com/paper/2408.07702/) (2024)
- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect](https://mlsanity.com/paper/2208.10099/) (2022)
- [Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing](https://mlsanity.com/paper/2103.02227/) (2021)
- [SQLBench: A Comprehensive Evaluation for Text-to-SQL Capabilities of Large Language Models](https://mlsanity.com/paper/2403.02951/) (2024)
- [SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising](https://mlsanity.com/paper/2105.07911/) (2021)
- [Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey](https://mlsanity.com/paper/2310.17894/) (2023)
- [PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency](https://mlsanity.com/paper/2403.09732/) (2024)
- [Improving Text-to-SQL with Schema Dependency Learning](https://mlsanity.com/paper/2103.04399/) (2021)
- [Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering](https://mlsanity.com/paper/2108.02866/) (2021)
- [UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL](https://mlsanity.com/paper/2203.07781/) (2022)
- [Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database](https://mlsanity.com/paper/2211.05165/) (2022)
- [Prompting GPT-3.5 for Text-to-SQL with De-semanticization and Skeleton Retrieval](https://mlsanity.com/paper/2304.13301/) (2023)
- [mRAT-SQL+GAP:A Portuguese Text-to-SQL Transformer](https://mlsanity.com/paper/2110.03546/) (2021)
- [Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errors](https://mlsanity.com/paper/2501.09310/) (2025)
- [Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain](https://mlsanity.com/paper/2307.05074/) (2023)
- [BASE-SQL: A powerful open source Text-To-SQL baseline approach](https://mlsanity.com/paper/2502.10739/) (2025)
- [LG AI Research & KAIST at EHRSQL 2024: Self-Training Large Language Models with Pseudo-Labeled Unanswerable Questions for a Reliable Text-to-SQL System on EHRs](https://mlsanity.com/paper/2405.11162/) (2024)
- [Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQL](https://mlsanity.com/paper/2112.08735/) (2021)
- [Leveraging Table Content for Zero-shot Text-to-SQL with Meta-Learning](https://mlsanity.com/paper/2109.05395/) (2021)
- [Korean-Specific Dataset for Table Question Answering](https://mlsanity.com/paper/2201.06223/) (2022)
- [A Mutual Information Maximization Approach for the Spurious Solution Problem in Weakly Supervised Question Answering](https://mlsanity.com/paper/2106.07174/) (2021)
- [Enhancing Open-Domain Table Question Answering via Syntax- and Structure-aware Dense Retrieval](https://mlsanity.com/paper/2309.10506/) (2023)
- [Taming SQL Complexity: LLM-Based Equivalence Evaluation for Text-to-SQL](https://mlsanity.com/paper/2506.09359/) (2025)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique](https://mlsanity.com/paper/2209.12177/) (2022)
- [Feature Representation Learning for NL2SQL Generation Based on Coupling and Decoupling](https://mlsanity.com/paper/2306.17646/) (2023)
- [AraSpider: Democratizing Arabic-to-SQL](https://mlsanity.com/paper/2402.07448/) (2024)

## Abstract (arXiv)

In this paper, we study how to leverage pre-trained language models in Text-to-SQL. We argue that previous approaches under utilize the base language models by concatenating all columns together with the NL question and feeding them into the base language model in the encoding stage. We propose a neat approach called Hybrid Ranking Network (HydraNet) which breaks down the problem into column-wise ranking and decoding and finally assembles the column-wise outputs into a SQL query by straightforward rules. In this approach, the encoder is given a NL question and one individual column, which perfectly aligns with the original tasks BERT/RoBERTa is trained on, and hence we avoid any ad-hoc pooling or additional encoding layers which are necessary in prior approaches. Experiments on the WikiSQL dataset show that the proposed approach is very effective, achieving the top place on the leaderboard.